Search Results

You are looking at 1 - 10 of 27 items for

  • Author or Editor: Upmanu Lall x
  • Refine by Access: All Content x
Clear All Modify Search
Scott Steinschneider
and
Upmanu Lall

Abstract

Tropical moisture exports (TMEs) may play an important role in extreme precipitation. An analysis of the spatiotemporal structure of precipitation associated with TMEs for the eastern United States at seasonal and daily time scales is presented. TME-based precipitation is characterized based on the change in specific humidity along TME tracks delineated in a Lagrangian analysis of the ERA-Interim dataset. The empirical orthogonal functions (EOFs) of seasonal TME-based precipitation are analyzed separately for each season to identify the dominant modes of interannual variability. Loading patterns for the first EOF show a distinct seasonal cycle in the core region of TME-based precipitation across the eastern United States, while the second EOF describes a northwest–southeast oscillation in the center of TME-induced precipitation occurrence. The EOFs for TMEs are compared against EOFs of gauged flood count data, which exhibit similar spatial structures. Correlations between TME EOFs, geopotential heights, and sea surface temperatures suggest a strong connection between TME-based precipitation, the Pacific–North American (PNA) pattern, Pacific decadal oscillation (PDO), and the Intra-Americas Sea patterns for much of the calendar year. Daily TME-based and total precipitation is projected onto the leading seasonal EOFs to examine the characteristics of upper-quantile daily events. The daily analysis suggests that the PNA can potentially provide information regarding heavy TME-based precipitation at a lead time of 1–10 days or more in most seasons and total precipitation in the winter. The potential for subseasonal, seasonal, and decadal forecasts or conditional simulations of precipitation in the study region is discussed.

Full access
Scott Steinschneider
and
Upmanu Lall

Abstract

This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using nonparametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA.

Full access
Christina Karamperidou
,
Francesco Cioffi
, and
Upmanu Lall

Abstract

Zonal and meridional surface temperature gradients are considered to be determinants of large-scale atmospheric circulation patterns. However, there has been limited investigation of these gradients as diagnostic aids. Here, the twentieth-century variability in the Northern Hemisphere equator-to-pole temperature gradient (EPG) and the ocean–land temperature contrast (OLC) is explored. A secular trend in decreasing EPG and OLC is noted. Decadal and interannual (ENSO-related) variations in the joint distribution of EPG and OLC are identified, hinting at multistable climate states that may be indigenous to the climate or due to changing boundary forcings. The NH circulation patterns for cases in the tails of the joint distribution of EPG and OLC are also seen to be different. Given this context, this paper extends past efforts to develop insights into jet stream dynamics using the Lorenz-1984 model, which is forced directly and only by EPG and OLC. The joint probability distribution of jet stream and eddy energy, conditional on EPG and OLC scenarios, is investigated. The scenarios correspond to (i) warmer versus colder climate conditions and (ii) polarized ENSO phases. The latter scenario involves the use of a heuristic ENSO model to drive the Lorenz-1984 model via a modulation of the EPG or the OLC. As with GCMs, the low-order model reveals that the response to El Niño forcing is not similar to an anthropogenic warming signature. The potential uses of EPG and OLC as macro-level indicators of climate change and variability and for comparing results across GCMs and observations are indicated.

Full access
Shida Gao
,
Pan Liu
, and
Upmanu Lall

Abstract

Integrated atmospheric water vapor transport (IVT) is a determinant of global precipitation. In this paper, using the CERA-20C climate reanalysis dataset, we explore three questions in Northern Hemisphere precipitation for four seasons: 1) What is the covariability between the leading spatiotemporal modes of seasonal sea surface temperature (SST), the seasonal IVT, and the seasonal precipitation for the Northern Hemisphere? 2) How well can the leading spatial modes of seasonal precipitation be reconstructed from the leading modes of IVT and SST for the same season? 3) How well can the leading modes of precipitation for the next season be predicted from the leading modes of the current season’s SST and IVT? Wavelet analyses identify covariation in the leading modes of seasonal precipitation and those of IVT and SST in the 2–8-yr band, with the highest amplitude in the March–May (MAM) season, and provide a firm physical explanation for the potential predictability. We find that a subset of the 10 leading principal components of the seasonal IVT and SST fields has significant trends in connections with seasonal precipitation modes, and provides an accurate statistical concurrent reconstruction and one-season-ahead forecast of the leading seasonal precipitation modes, thus providing a pathway to improving the understanding and prediction of precipitation extremes in the context of climate change attribution, seasonal and longer prediction, and climate change scenarios. The same-season reconstruction model can explain 76% of the variance, and the next-season forecast model can explain 58% variance of hemispheric precipitation on average.

Full access
A. Sankarasubramanian
,
Upmanu Lall
, and
Susan Espinueva

Abstract

Seasonal streamflow forecasts contingent on climate information are essential for water resources planning and management as well as for setting up contingency measures during extreme years. In this study, operational streamflow forecasts are developed for a reservoir system in the Philippines using ECHAM4.5 precipitation forecasts (EPF) obtained using persisted sea surface temperature (SST) scenarios. Diagnostic analyses on SST conditions show that the tropical SSTs influence the streamflow during extreme years, whereas the local SSTs (0°–25°N, 115°–130°E) account for streamflow variability during normal years. Given that the EPF, local, and tropical SST conditions are spatially correlated, principal components regression (PCR) is employed to downscale the GCM-predicted precipitation fields and SST anomalies to monthly streamflow forecasts and to update them every month within the season using the updated EPF and SST conditions. These updated forecasts improve the prediction of monthly streamflows within the season in comparison to the skill of the monthly streamflow forecasts issued at the beginning of the season. It is also shown that the streamflow forecasting model developed using EPF under persisted SST conditions performs well upon employing EPF obtained under predicted SSTs as predictor. This has potential implications in the development of operational streamflow forecasts and statistical downscaling, which requires adequate years of retrospective GCM forecasts for recalibration. Finally, the study also shows that predicting the seasonal streamflow using the monthly precipitation forecasts reproduces the observed seasonal total better than the conventional approach of using seasonal precipitation forecasts to predict the seasonal streamflow.

Full access
Balaji Rajagopalan
,
Upmanu Lall
, and
Stephen E. Zebiak

Abstract

A Bayesian methodology is used to assess the information content of categorical, probabilistic forecasts of specific variables derived from a general circulation model (GCM) forecast ensemble, and to combine a “prior” forecast (climatological probabilities of each category) with a categorical probabilistic forecast derived from a GCM ensemble to develop posterior, or “regularized” categorical probabilities. The combination algorithm assigns a weight to a particular model forecast and to climatology. The ratio of the sample likelihood of the model based on the posterior categorical probabilities, to that based on climatological probabilities, computed over the period of record of historical forecasts, provides a measure of the skill or information content of a candidate model. The weight given to a GCM forecast serves as a secondary indicator of its information content. Model weights are determined by maximizing the likelihood ratio. Results using the so-called ranked probability skill score as an objective function are also obtained, and are found to be very similar to the likelihood-based results.

The procedure is extended to the optimal combination of forecasts from multiple GCMs. An application of the method is presented for global, seasonal precipitation and temperature forecasts in two different seasons, based on 41 yr of observational and model simulation data. The multimodel combination skill is significantly better than climatology skill in only a few regions of the globe, but is generally an improvement over individual models, and over a simple average of forecasts from different models. Limitations and possible improvements of the methodology are discussed.

Full access
Balaji Rajagopalan
,
Michael E. Mann
, and
Upmanu Lall

Abstract

Guided by the increasing awareness and detectability of spatiotemporally organized climatic variability at interannual and longer timescales, the authors motivate the paradigm of a climate system that exhibits excitations of quasi-oscillatory eigenmodes with characteristic timescales and large-scale spatial patterns of coherence. It is assumed that any such modes are superposed on a spatially and temporally autocorrelated stochastic noise background. Under such a paradigm, a previously described (Mann and Park) multivariate frequency-domain approach is promoted as a particularly effective means of spatiotemporal signal identification and reconstruction, and an associated forecasting methodology is introduced. This combined signal detection/forecasting scheme exhibits significantly greater skill than conventional forecasting approaches in the context of a synthetic example consistent with the adopted paradigm. The example application demonstrates statistically significant skill at 5–10-yr lead times. Applications to operational long-range climatic forecasting are motivated and discussed.

Full access
A. Sankarasubramanian
,
Upmanu Lall
,
Naresh Devineni
, and
Susan Espinueva

Abstract

Seasonal streamflow forecasts contingent on climate information are essential for short-term planning (e.g., water allocation) and for setting up contingency measures during extreme years. However, the water allocated based on the climate forecasts issued at the beginning of the season needs to be revised using the updated climate forecasts throughout the season. In this study, reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with “persisted” SSTs were used to improve both seasonal and intraseasonal water allocation during the October–February season for the Angat reservoir, a multipurpose system, in the Philippines. Monthly updated reservoir inflow forecasts are ingested into a reservoir simulation model to allocate water for multiple uses by ensuring a high probability of meeting the end-of-season target storage that is required to meet the summer (March–May) demand. The forecast-based allocation is combined with the observed inflows during the season to estimate storages, spill, and generated hydropower from the system. The performance of the reservoir is compared under three scenarios: forecasts issued at the beginning of the season, monthly updated forecasts during the season, and use of climatological values. Retrospective reservoir analysis shows that the operation of a reservoir by using monthly updated inflow forecasts reduces the spill considerably by increasing the allocation for hydropower during above-normal-inflow years. During below-normal-inflow years, monthly updated streamflow forecasts could be effectively used for ensuring enough water for the summer season by meeting the end-of-season target storage. These analyses suggest the importance of performing experimental reservoir analyses to understand the potential challenges and opportunities in improving seasonal and intraseasonal water allocation by using real-time climate forecasts.

Full access
Balaji Rajagopalan
,
Upmanu Lall
, and
Mark A. Cane

Abstract

There has been an apparent increase in the frequency and duration of El Niño–Southern Oscillation events in the last two decades relative to the prior period of record. Furthermore, 1990–95 was the longest period of sustained high Darwin sea level pressure in the instrumental record. Variations in the frequency and duration of such events are of considerable interest because of their implications for understanding global climatic variability and also the possibility that the climate system may be changing due to external factors such as the increased concentration of greenhouse gases in the atmosphere. Nonparametric statistical methods for time series analysis are applied to a 1882 to 1995 seasonal Darwin sea level pressure (DSLP) anomaly time series to explore the variations in El Niño–like anomaly occurrence and persistence over the period of record. Return periods for the duration of the 1990–95 event are estimated to be considerably smaller than those recently obtained by using a linear ARMA model with the same time series. The likelihood of a positive anomaly of the DSLP, as well as its persistence, is found to exhibit decadal- to centennial-scale variability and was nearly as high at the end of the last century as it has been recently. The 1990–95 event has a much lower return period if the analysis is based on the 1882–1921 DSLP data. The authors suggest that conclusions that the 1990–95 event may be an effect of greenhouse gas–induced warming be tempered by a recognition of the natural variability in the system.

Full access
Naresh Devineni
,
Upmanu Lall
,
Neil Pederson
, and
Edward Cook

Abstract

A hierarchical Bayesian regression model is presented for reconstructing the average summer streamflow at five gauges in the Delaware River basin using eight regional tree-ring chronologies. The model provides estimates of the posterior probability distribution of each reconstructed streamflow series considering parameter uncertainty. The vectors of regression coefficients are modeled as draws from a common multivariate normal distribution with unknown parameters estimated as part of the analysis. This leads to a multilevel structure. The covariance structure of the streamflow residuals across sites is explicitly modeled. The resulting partial pooling of information across multiple stations leads to a reduction in parameter uncertainty. The effect of no pooling and full pooling of station information, as end points of the method, is explored. The no-pooling model considers independent estimation of the regression coefficients for each streamflow gauge with respect to each tree-ring chronology. The full-pooling model considers that the same regression coefficients apply across all streamflow sites for a particular tree-ring chronology. The cross-site correlation of residuals is modeled in all cases. Performance on metrics typically used by tree-ring reconstruction experts, such as reduction of error, coefficient of efficiency, and coverage rates under credible intervals is comparable to, or better, for the partial-pooling model relative to the no-pooling model, and streamflow estimation uncertainty is reduced. Long record simulations from reconstructions are used to develop estimates of the probability of duration and severity of droughts in the region. Analysis of monotonic trends in the reconstructed drought events do not reject the null hypothesis of no trend at the 90% significance over 1754–2000.

Full access